Using all genes
names(ls_preprocessed)
## [1] "p_all" "rna_all" "pData_rnaseq" "counts_all" "vsd_mat"
## [6] "pbatch_bf" "pgender_bf" "pbatch_af" "pgender_af"
dim(ls_preprocessed$vsd_mat)
## [1] 37984 82
corr_pt <- Hmisc::rcorr(ls_preprocessed$vsd_mat, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 3,
row_km = 3,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)

Using top n genes
n_genes <- 10000
vsd_mat <- ls_preprocessed$vsd_mat
variances <- apply(vsd_mat, 1, var)
top_genes <- data.frame(vsd_mat) %>%
mutate(gene=rownames(.),
symbol=ls_preprocessed$rna_all$Feature_gene_name,
variances = variances) %>%
arrange(desc(variances)) %>%
dplyr::select(gene, symbol) %>%
head(n_genes)
vsd_mat5k<- vsd_mat[top_genes$gene,]
rownames(vsd_mat5k) <- top_genes$symbol
corr_pt <- Hmisc::rcorr(vsd_mat5k, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)

n_genes <- 5000
vsd_mat <- ls_preprocessed$vsd_mat
variances <- apply(vsd_mat, 1, var)
top_genes <- data.frame(vsd_mat) %>%
mutate(gene=rownames(.),
symbol=ls_preprocessed$rna_all$Feature_gene_name,
variances = variances) %>%
arrange(desc(variances)) %>%
dplyr::select(gene, symbol) %>%
head(n_genes)
vsd_mat5k<- vsd_mat[top_genes$gene,]
rownames(vsd_mat5k) <- top_genes$symbol
corr_pt <- Hmisc::rcorr(vsd_mat5k, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)

n_genes <- 1000
vsd_mat <- ls_preprocessed$vsd_mat
variances <- apply(vsd_mat, 1, var)
top_genes <- data.frame(vsd_mat) %>%
mutate(gene=rownames(.),
symbol=ls_preprocessed$rna_all$Feature_gene_name,
variances = variances) %>%
arrange(desc(variances)) %>%
dplyr::select(gene, symbol) %>%
head(n_genes)
vsd_mat5k<- vsd_mat[top_genes$gene,]
rownames(vsd_mat5k) <- top_genes$symbol
corr_pt <- Hmisc::rcorr(vsd_mat5k, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)

n_genes <- 500
vsd_mat <- ls_preprocessed$vsd_mat
variances <- apply(vsd_mat, 1, var)
top_genes <- data.frame(vsd_mat) %>%
mutate(gene=rownames(.),
symbol=ls_preprocessed$rna_all$Feature_gene_name,
variances = variances) %>%
arrange(desc(variances)) %>%
dplyr::select(gene, symbol) %>%
head(n_genes)
vsd_mat5k<- vsd_mat[top_genes$gene,]
rownames(vsd_mat5k) <- top_genes$symbol
corr_pt <- Hmisc::rcorr(vsd_mat5k, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)

n_genes <- 100
vsd_mat <- ls_preprocessed$vsd_mat
variances <- apply(vsd_mat, 1, var)
top_genes <- data.frame(vsd_mat) %>%
mutate(gene=rownames(.),
symbol=ls_preprocessed$rna_all$Feature_gene_name,
variances = variances) %>%
arrange(desc(variances)) %>%
dplyr::select(gene, symbol) %>%
head(n_genes)
vsd_mat5k<- vsd_mat[top_genes$gene,]
rownames(vsd_mat5k) <- top_genes$symbol
corr_pt <- Hmisc::rcorr(vsd_mat5k, type = 'spearman')
Heatmap(corr_pt$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

corr_genes <- Hmisc::rcorr(t(vsd_mat5k), type = 'spearman')
Heatmap(corr_genes$r, name = "mat",
column_km = 2,
row_km = 2,
heatmap_legend_param = list(color_bar = "continuous"),
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))

# Hierarchical clustering
d <- dist(corr_pt$r)
hc1 <- hclust(d)
plot(hc1, cex = 0.6, hang = -1)
